In recent years, the sharing economy has become popular, with outstanding examples
such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods
and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus,
an essential component of its success is its capability to build trust among strangers. This goal is
achieved usually by creating reputation systems where users rate each other after each transaction.
Nevertheless, these systems present challenges such as the lack of information about new users or
the reliability of peer ratings. However, users leave their digital footprints on many social networks.
These social footprints are used for inferring personal information (e.g., personality and consumer
habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the
art on reputation systems by researching how digital footprints coming from social networks can be
used to predict future behaviors on sharing economy platforms. In particular, we have focused on
predicting the reputation of users in the second-hand market Wallapop based solely on their users’
Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction
model based on social data; and (b) an anonymized dataset of paired users in the sharing economy
site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.